Scene-based non-uniformity correction: From algorithm to implementation on a smart camera

Toczek, T.; Hamdi, Fatma; Heyrman, Barthélémy; Dubois, Julien; Mitéran, Johel; Ginhac, Dominique

doi:10.1016/j.sysarc.2013.05.017

Cited by 8 publications

(5 citation statements)

References 40 publications

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“…Several techniques are reported in the literature to correct the image through NUC techniques. These techniques can be broadly classified into: (i) Blackbody calibration method based 4,5 (ii) Scene information based 6,7,8 .…”

Section: Basic Operation Of Non Uniformitymentioning

confidence: 99%

Real Time Non uniformity Correction Algorithm and Implementation in Reconfigurable Architecture for Infra red Imaging Systems

2019

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In modern electro-optical systems, infra-red (IR) imaging system is an essential sensor used for day and night surveillance. In recent years, advancements in IR sensor technology resulted the detectors having smaller pitch, better thermal sensitivity with large format like 640.512, 1024.768 and 1280.1024. Large format IR detectors enables realisation of high resolution compact thermal imager having wide field-of view coverage. However, the performance of these infrared imaging systems gets limited by non uniformity produced by sensing element, which is temporal in nature and present in spatial domain. This non uniformity results the fixed pattern noise, which arises due to variation in gain and offset components of the each pixel of the sensor even when exposed to a uniform scene. This fixed pattern noise limits the temperature resolution capability of the IR imaging system thereby causing the degradation in system performance. Therefore, it is necessary to correct the non-uniformities in real time. In this paper, non uniformity correction algorithm and its implementation in reconfigurable architectures have been presented and results on real time data have been described.

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Section: Basic Operation Of Non Uniformitymentioning

confidence: 99%

Real Time Non uniformity Correction Algorithm and Implementation in Reconfigurable Architecture for Infra red Imaging Systems

2019

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“…The complete architecture can process up to 100 fps on frames of 320 Â 240 pixels using around 38% of the logic resources of the FPGA, and consumes 832 mW. Toczek developed a hardware implementation of an improved version of the constant statistics algorithm [24]. This method compensated the offset by removing the spatial low frequencies of each frame before applying the constant statistics technique, and then added the low frequencies to the final image.…”

Section: Related Workmentioning

confidence: 99%

Embedded nonuniformity correction in infrared focal plane arrays using the Constant Range algorithm

Redlich

Figueroa

Torres

et al. 2015

Infrared Physics & Technology

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“…During postprocessing, dark frames were subtracted from the corresponding images-of-interest to reduce fixed-pattern noise (FPN) and as a replacement for the non-uniformity correction (NUC) [11]. The effectiveness of this technique was guaranteed as long as measurement frames and dark frames were taken in close succession [12]. To reduce detector noise, depending upon the data set, each recorded image consisted of the average of 180-500 Manipulation of a detector's dynamic range via the choice of IT.…”

Section: Experimental Setup and Proceduresmentioning

confidence: 99%

Empirical compensation of reciprocity failure and integration time nonlinearity in a mid-wave infrared camera

Romm¹,

Lev²,

Cukurel³

2015

Meas. Sci. Technol.

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Thermal-infrared radiation measurements, conducted using an InSb camera, indicated a failure of the reciprocity law for a wide range of radiation intensities and integration times. When reciprocity between radiation flux and integration time was assumed, the radiation estimates, computed from different combinations of output signals and selected integration time values, suffered from imprecisions of up to 12%. Temperature errors of ~4% were predicted for low emissivity surfaces, at all temperatures. A novel empirical methodology, which compensates for multiple nonlinearity effects, is presented. Among different types of models, it is demonstrated that an equation, which represents a power-law dependence of the output signal on integration time best describes the physical system. Experimental procedures are suggested to avoid nonlinearity-related errors.

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Scene-based non-uniformity correction: From algorithm to implementation on a smart camera

Cited by 8 publications

References 40 publications

Real Time Non uniformity Correction Algorithm and Implementation in Reconfigurable Architecture for Infra red Imaging Systems

Real Time Non uniformity Correction Algorithm and Implementation in Reconfigurable Architecture for Infra red Imaging Systems

Embedded nonuniformity correction in infrared focal plane arrays using the Constant Range algorithm

Empirical compensation of reciprocity failure and integration time nonlinearity in a mid-wave infrared camera

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